Discovering future disaster management capability needs using scenario planning

In recent years disaster risk reduction efforts have focused on disturbances ranging from climate variability, seismic hazards, geo-political instability and public and animal health crises. These factors combined with uncertainty derived from inter-dependencies within and across systems of critical infrastructure create significant problems of governance for the private and public sector alike. The potential for rapid spread of impacts, geographically and virtually, can render a comprehensive understanding of disaster response and recovery needs and risk mitigation issues beyond the grasp of competent authority. Because of such cascading effects communities and governments at local and state-levels are unlikely to face single incidents but rather series of systemic impacts: often appearing concurrently. A further point to note is that both natural and technological hazards can act directly on socio-technical systems as well as being propagated by them: as network events. Such events have been categorised as ‘outside of the box,’ ‘too fast,’ and ‘too strange’ (Lagadec, 2004). Emergent complexities in linked systems can make disaster effects difficult to anticipate and recovery efforts difficult to plan for. Beyond the uncertainties of real world disasters, that might be called familiar or even regular, can we safely assume that the generic capability we use now will suit future disaster contexts? This paper presents initial scoping of research funded by the Bushfire and Natural Hazards Cooperative Research Centre seeking to define future capability needs of disaster management organisations. It explores challenges to anticipating the needs of representative agencies and groups active in before, during and after phases of emergency and disaster situations using capability deficit assessments and scenario assessment.

Three-dimensional printing in the construction industry: A review

3D printing (3Dp) has long been used in the manufacturing sector as a way to automate, accelerate production and reduce waste materials. It is able to build a wide variety of objects if the necessary specifications are provided to the printer and no problems are presented by the limited range of materials available. With 3Dp becoming cheaper, more reliable and, as a result, more prevalent in the world at large, it may soon make inroads into the construction industry. Little is known however, of 3Dp in current use the construction industry and its potential for the future and this paper seeks to rectify this situation by providing a review of the relevant literature. In doing this, the three main 3Dp methods of contour crafting, concrete printing and D-shape 3Dp are described which, as opposed to the traditional construction method of cutting materials down to size, deliver only what is needed for completion, vastly reducing waste. Also identified is 3Dp’s potential to enable buildings to be constructed many times faster and with significantly reduced labour costs. In addition, it is clear that construction 3Dp can allow the further inclusion of Building Information Modelling into the construction process - streamlining and improving the scheduling requirements of a project. However, current 3Dp processes are known to be costly, unsuited to large-scale products and conventional design approaches, and have a very limited range of materials that can be used. Moreover, the only successful examples of construction in action to date have occurred in controlled laboratory environments and, as real world trials have yet to be completed, it is yet to be seen whether it can be it equally proficient in practical situations. Key Words: 3D Printing; Contour Crafting; Concrete Printing; D-shape; Building Automation.

Shortest path and vehicle trajectory aided map-matching for low frequency GPS data

Map-matching algorithms that utilise road segment connectivity along with other data (i.e.position, speed and heading) in the process of map-matching are normally suitable for high frequency (1 Hz or higher) positioning data from GPS. While applying such map-matching algorithms to low frequency data (such as data from a fleet of private cars, buses or light duty vehicles or smartphones), the performance of these algorithms reduces to in the region of 70% in terms of correct link identification, especially in urban and sub-urban road networks. This level of performance may be insufficient for some real-time Intelligent Transport System (ITS) applications and services such as estimating link travel time and speed from low frequency GPS data. Therefore, this paper develops a new weight-based shortest path and vehicle trajectory aided map-matching (stMM) algorithm that enhances the map-matching of low frequency positioning data on a road map. The well-known A* search algorithm is employed to derive the shortest path between two points while taking into account both link connectivity and turn restrictions at junctions. In the developed stMM algorithm, two additional weights related to the shortest path and vehicle trajectory are considered: one shortest path-based weight is related to the distance along the shortest path and the distance along the vehicle trajectory, while the other is associated with the heading difference of the vehicle trajectory. The developed stMM algorithm is tested using a series of real-world datasets of varying frequencies (i.e. 1 s, 5 s, 30 s, 60 s sampling intervals). A high-accuracy integrated navigation system (a high-grade inertial navigation system and a carrier-phase GPS receiver) is used to measure the accuracy of the developed algorithm. The results suggest that the algorithm identifies 98.9% of the links correctly for every 30 s GPS data. Omitting the information from the shortest path and vehicle trajectory, the accuracy of the algorithm reduces to about 73% in terms of correct link identification. The algorithm can process on average 50 positioning fixes per second making it suitable for real-time ITS applications and services.

Development of a cost-effective and flexible vibration DAQ system for long-term continuous structural health monitoring

In the structural health monitoring (SHM) field, long-term continuous vibration-based monitoring is becoming increasingly popular as this could keep track of the health status of structures during their service lives. However, implementing such a system is not always feasible due to on-going conflicts between budget constraints and the need of sophisticated systems to monitor real-world structures under their demanding in-service conditions. To address this problem, this paper presents a comprehensive development of a cost-effective and flexible vibration DAQ system for long-term continuous SHM of a newly constructed institutional complex with a special focus on the main building. First, selections of sensor type and sensor positions are scrutinized to overcome adversities such as low-frequency and low-level vibration measurements. In order to economically tackle the sparse measurement problem, a cost-optimized Ethernet-based peripheral DAQ model is first adopted to form the system skeleton. A combination of a high-resolution timing coordination method based on the TCP/IP command communication medium and a periodic system resynchronization strategy is then proposed to synchronize data from multiple distributed DAQ units. The results of both experimental evaluations and experimental–numerical verifications show that the proposed DAQ system in general and the data synchronization solution in particular work well and they can provide a promising cost-effective and flexible alternative for use in real-world SHM projects. Finally, the paper demonstrates simple but effective ways to make use of the developed monitoring system for long-term continuous structural health evaluation as well as to use the instrumented building herein as a multi-purpose benchmark structure for studying not only practical SHM problems but also synchronization related issues.

Copula-based spatial modelling of geometallurgical variables

The most important aspect of modelling a geological variable, such as metal grade, is the spatial correlation. Spatial correlation describes the relationship between realisations of a geological variable sampled at different locations. Any method for spatially modelling such a variable should be capable of accurately estimating the true spatial correlation. Conventional kriged models are the most commonly used in mining for estimating grade or other variables at unsampled locations, and these models use the variogram or covariance function to model the spatial correlations in the process of estimation. However, this usage assumes the relationships of the observations of the variable of interest at nearby locations are only influenced by the vector distance between the locations. This means that these models assume linear spatial correlation of grade. In reality, the relationship with an observation of grade at a nearby location may be influenced by both distance between the locations and the value of the observations (ie non-linear spatial correlation, such as may exist for variables of interest in geometallurgy). Hence this may lead to inaccurate estimation of the ore reserve if a kriged model is used for estimating grade of unsampled locations when nonlinear spatial correlation is present. Copula-based methods, which are widely used in financial and actuarial modelling to quantify the non-linear dependence structures, may offer a solution. This method was introduced by Bárdossy and Li (2008) to geostatistical modelling to quantify the non-linear spatial dependence structure in a groundwater quality measurement network. Their copula-based spatial modelling is applied in this research paper to estimate the grade of 3D blocks. Furthermore, real-world mining data is used to validate this model. These copula-based grade estimates are compared with the results of conventional ordinary and lognormal kriging to present the reliability of this method.

Role of rainfall and catchment characteristics on urban stormwater quality

Urbanisation significantly changes the characteristics of a catchment as natural areas are transformed to impervious surfaces such as roads, roofs and parking lots. The increased fraction of impervious surfaces leads to changes to the stormwater runoff characteristics, whilst a variety of anthropogenic activities common to urban areas generate a range of pollutants such as nutrients, solids and organic matter. These pollutants accumulate on catchment surfaces and are removed and trans- ported by stormwater runoff and thereby contribute pollutant loads to receiving waters. In summary, urbanisation influences the stormwater characteristics of a catchment, including hydrology and water quality. Due to the growing recognition that stormwater pollution is a significant environmental problem, the implementation of mitigation strategies to improve the quality of stormwater runoff is becoming increasingly common in urban areas. A scientifically robust stormwater quality treatment strategy is an essential requirement for effective urban stormwater management. The efficient design of treatment systems is closely dependent on the state of knowledge in relation to the primary factors influencing stormwater quality. In this regard, stormwater modelling outcomes provide designers with important guidance and datasets which significantly underpin the design of effective stormwater treatment systems. Therefore, the accuracy of modelling approaches and the reliability modelling outcomes are of particular concern. This book discusses the inherent complexity and key characteristics in the areas of urban hydrology and stormwater quality, based on the influence exerted by a range of rainfall and catchment characteristics. A comprehensive field sampling and testing programme in relation to pollutant build-up, an urban catchment monitoring programme in relation to stormwater quality and the outcomes from advanced statistical analyses provided the platform for the knowledge creation. Two case studies and two real-world applications are discussed to illustrate the translation of the knowledge created to practical use in relation to the role of rainfall and catchment characteristics on urban stormwater quality. An innovative rainfall classification based on stormwater quality was developed to support the effective and scientifically robust design of stormwater treatment systems. Underpinned by the rainfall classification methodology, a reliable approach for design rainfall selection is proposed in order to optimise stormwater treatment based on both, stormwater quality and quantity. This is a paradigm shift from the common approach where stormwater treatment systems are designed based solely on stormwater quantity data. Additionally, how pollutant build-up and stormwater runoff quality vary with a range of catchment characteristics was also investigated. Based on the study out- comes, it can be concluded that the use of only a limited number of catchment parameters such as land use and impervious surface percentage, as it is the case in current modelling approaches, could result in appreciable error in water quality estimation. Influential factors which should be incorporated into modelling in relation to catchment characteristics, should also include urban form and impervious surface area distribution. The knowledge created through the research investigations discussed in this monograph is expected to make a significant contribution to engineering practice such as hydrologic and stormwater quality modelling, stormwater treatment design and urban planning, as the study outcomes provide practical approaches and recommendations for urban stormwater quality enhancement. Furthermore, this monograph also demonstrates how fundamental knowledge of stormwater quality processes can be translated to provide guidance on engineering practice, the comprehensive application of multivariate data analyses techniques and a paradigm on integrative use of computer models and mathematical models to derive practical outcomes.

An application of fuzzy DL-Based semantic perception to soil container classification

Semantic perception and object labeling are key requirements for robots interacting with objects on a higher level. Symbolic annotation of objects allows the usage of planning algorithms for object interaction, for instance in a typical fetchand-carry scenario. In current research, perception is usually based on 3D scene reconstruction and geometric model matching, where trained features are matched with a 3D sample point cloud. In this work we propose a semantic perception method which is based on spatio-semantic features. These features are defined in a natural, symbolic way, such as geometry and spatial relation. In contrast to point-based model matching methods, a spatial ontology is used where objects are rather described how they "look like", similar to how a human would described unknown objects to another person. A fuzzy based reasoning approach matches perceivable features with a spatial ontology of the objects. The approach provides a method which is able to deal with senor noise and occlusions. Another advantage is that no training phase is needed in order to learn object features. The use-case of the proposed method is the detection of soil sample containers in an outdoor environment which have to be collected by a mobile robot. The approach is verified using real world experiments.

Towards robust automatic affective classification of images using facial expressions for practical applications

Affect is an important feature of multimedia content and conveys valuable information for multimedia indexing and retrieval. Most existing studies for affective content analysis are limited to low-level features or mid-level representations, and are generally criticized for their incapacity to address the gap between low-level features and high-level human affective perception. The facial expressions of subjects in images carry important semantic information that can substantially influence human affective perception, but have been seldom investigated for affective classification of facial images towards practical applications. This paper presents an automatic image emotion detector (IED) for affective classification of practical (or non-laboratory) data using facial expressions, where a lot of “real-world” challenges are present, including pose, illumination, and size variations etc. The proposed method is novel, with its framework designed specifically to overcome these challenges using multi-view versions of face and fiducial point detectors, and a combination of point-based texture and geometry. Performance comparisons of several key parameters of relevant algorithms are conducted to explore the optimum parameters for high accuracy and fast computation speed. A comprehensive set of experiments with existing and new datasets, shows that the method is effective despite pose variations, fast, and appropriate for large-scale data, and as accurate as the method with state-of-the-art performance on laboratory-based data. The proposed method was also applied to affective classification of images from the British Broadcast Corporation (BBC) in a task typical for a practical application providing some valuable insights.

Analysis and improvement of a construction permit approval process: A teaching case for developing business process development capabilities, targeting developing nations

With the increasing competitiveness in global markets, many developing nations are striving to constantly improve their services in search for the next competitive edge. As a result, the demand and need for Business Process Management (BPM) in these regions is seeing a rapid rise. Yet there exists a lack of professional expertise and knowledge to cater to that need. Therefore, the development of well-structured BPM training/ education programs has become an urgent requirement for these industries. Furthermore, the lack of textbooks or other self-educating material, that go beyond the basics of BPM, further ratifies the need for case based teaching and related cases that enable the next generation of professionals in these countries. Teaching cases create an authentic learning environment where complexities and challenges of the ‘real world’ can be presented in a narrative, enabling students to evolve crucial skills such as problem analysis, problem solving, creativity within constraints as well as the application of appropriate tools (BPMN) and techniques (including best practices and benchmarking) within richer and real scenarios. The aim of this paper is to provide a comprehensive teaching case demonstrating the means to tackle any developing nation’s legacy government process undermined by inefficiency and ineffectiveness. The paper also includes thorough teaching notes The article is presented in three main parts: (i) Introduction - that provides a brief background setting the context of this paper, (ii) The Teaching Case, and (iii) Teaching notes.

School based experiences as contributors to career decision-making: Findings from a cross-sectional survey of high-school students

This paper is based on a study examining the impact of young people’s backgrounds and educational experiences on career choice capability with the aim of informing education policy. A total of 706 students from secondary schools (Years 9-12) in New South Wales, Australia took part in an online survey. This paper focuses on the differences found between groups on the basis of their educational experiences. Participants who were uncertain of their future career plans were more likely to attend non-selective, non-metropolitan schools and were more likely to hold negative attitudes towards school. Career ‘uncertain’ students were also less likely to be satisfied with the elective subjects offered at their school and reported less access to career education sessions. It is concluded that timely career information and guidance should be provided to students and their families in order to allow them to more meaningfully make use of the resources and opportunities available to them with a view toward converting these into real world benefits.

A review on prognostic techniques for non-stationary and non-linear rotating systems

The field of prognostics has attracted significant interest from the research community in recent times. Prognostics enables the prediction of failures in machines resulting in benefits to plant operators such as shorter downtimes, higher operation reliability, reduced operations and maintenance cost, and more effective maintenance and logistics planning. Prognostic systems have been successfully deployed for the monitoring of relatively simple rotating machines. However, machines and associated systems today are increasingly complex. As such, there is an urgent need to develop prognostic techniques for such complex systems operating in the real world. This review paper focuses on prognostic techniques that can be applied to rotating machinery operating under non-linear and non-stationary conditions. The general concept of these techniques, the pros and cons of applying these methods, as well as their applications in the research field are discussed. Finally, the opportunities and challenges in implementing prognostic systems and developing effective techniques for monitoring machines operating under non-stationary and non-linear conditions are also discussed.

Using academagogy to meet the needs of millennial learners: A comparative case study

Higher education institutions across the world are experiencing a new generation of students, known as millennial learners. They are more technologically literate and digitally connected than previous generations of learners. To meet the teaching and learning needs of these learners, we must offer more deliberate and meaningful learning experiences and opportunities, where students can see the connections between new material and their own experiences and real world applications – an academagogic approach. This study compares the implementation of academagogy for two different groups of millennial learners – one a traditional face-to-face undergraduate Engineering unit, and the other a mixed-mode (online and face-to-face) undergraduate Design unit. The units are discussed in terms of their student evaluation results, both qualitative and quantitative, and in terms of their academic outcomes for students. Conclusions are drawn about the applicability of academagogy as a heuristic for improving teaching and learning across disciplines, as well as its strengths and limitations in terms of student results.

The inner quality of an article: Will time tell?

In this paper, we assess whether quality survives the test of time in academia by comparing up to 80 years of academic journal article citations from two top journals, Econometrica and the American Economic Review. The research setting under analysis is analogous to a controlled real world experiment in that it involves a homogeneous task (trying to publish in top journals) by individuals with a homogenous job profile (academics) in a specific research environment (economics and econometrics). Comparing articles published concurrently in the same outlet at the same time (same issue) indicates that symbolic capital or power due to institutional affiliation or connection does seem to boost citation success at the beginning, giving those educated at or affiliated with leading universities an initial comparative advantage. Such advantage, however, does not hold in the long run: at a later stage,the publications of other researchers become as or even more successful.

Bayesian computational methods for spatial analysis of images

This thesis introduces a new way of using prior information in a spatial model and develops scalable algorithms for fitting this model to large imaging datasets. These methods are employed for image-guided radiation therapy and satellite based classification of land use and water quality. This study has utilized a pre-computation step to achieve a hundredfold improvement in the elapsed runtime for model fitting. This makes it much more feasible to apply these models to real-world problems, and enables full Bayesian inference for images with a million or more pixels.

Case Studies in Bayesian Statistical Modelling and Analysis

Provides an accessible foundation to Bayesian analysis using real world models This book aims to present an introduction to Bayesian modelling and computation, by considering real case studies drawn from diverse fields spanning ecology, health, genetics and finance. Each chapter comprises a description of the problem, the corresponding model, the computational method, results and inferences as well as the issues that arise in the implementation of these approaches. Case Studies in Bayesian Statistical Modelling and Analysis: •Illustrates how to do Bayesian analysis in a clear and concise manner using real-world problems. •Each chapter focuses on a real-world problem and describes the way in which the problem may be analysed using Bayesian methods. •Features approaches that can be used in a wide area of application, such as, health, the environment, genetics, information science, medicine, biology, industry and remote sensing. Case Studies in Bayesian Statistical Modelling and Analysis is aimed at statisticians, researchers and practitioners who have some expertise in statistical modelling and analysis, and some understanding of the basics of Bayesian statistics, but little experience in its application. Graduate students of statistics and biostatistics will also find this book beneficial.